Who is Predictive Lead Scoring For?

You might be reading about predictive lead scoring and wondering if it's something that's right for you. It can sound a little daunting and something that is for only for the very data-savvy. But that's not necessarily the case. Read on to find out if predictive lead scoring is for you!

You may have heard of predictive lead scoring in the context of technology companies. While, they are a large part of the cohort adopting predictive lead scoring, they don’t need to be the only ones taking advantage of this technology! Any organisation who can capture, use and manage data effectively can adopt predictive lead scoring.

Within an organisation, you might also be wondering if this is just a “marketing-department-thing”. The truth is that predictive lead scoring delivers excellent benefits to marketing and sales teams across the board. So whether you’re in sales, or marketing, or even management – predictive lead scoring may benefit your organization. In this article, I describe seven business scenarios (or “problems to solve”) that can make the implementation of predictive lead scoring a worthy endeavor. If any of these resonate with you (“yes please!”), then predictive lead scoring is worth a shot!

1. You want to improve lead nurturing across the entire funnel

Traditional lead scoring tends to focus on the end of the conversion funnel (closing the deal). A key advantage of predictive lead scoring over traditional scoring is that it can help you identify where your sales and marketing can improve from the very beginning of the customer journey. It can be used to nurture leads along the funnel (which by the way, can be completely non-linear, complex and not-funnel-like at all). Because predictive lead scoring is dynamic, it can adapt to real-time behaviors and facilitate lead engagement by automating certain tasks (for example, specific customer follow-up emails can be triggered by a particular action). It can also help provide tailored and relevant information for wherever your customer is in their journey (i.e. well-timed automated marketing).

2. You want to get richer data on your leads

Predictive lead scoring is not only about delivering a score to the sales team. The process of developing a predictive model more often than not involves integrating your existing data (e.g. from your CRM or your internal tracking website activity data) with other information from third-party online sources like Google Analytics or LinkedIn. Thus, unlike traditional lead scoring, predictive lead scoring is enriched with additional external information about a prospect – helping to build a more complete and therefore accurate picture of where your lead is. Furthermore, the most relevant information (i.e. the information that has greater predictive influence) can be prioritized in the lead’s profile (as determined by the model).

3. You want to improve and/or validate your business strategy

One of the main goals of predictive lead scoring is to build a picture of your ideal customer. In turn, the marketing team can use that information to improve their market segmentation or even validate their existing segmentation. Again, one of the beauties of machine learning is that it is constantly self-improving and can provide real-time feedback, so it can help marketer’s quickly adapt new strategies. This is very useful when operating in a changing market (with new products and shifting customer needs). Improving your business strategy is vital for continued growth.

4. You have grown sufficiently that you need an automated way to process leads

Generally lead scoring is not appropriate for a start-up. If you are a growing business, you may find yourself in a situation where there are more leads you can effectively process manually (i.e. on a case-by-case basis). Equally, marketing may be in a situation where they need to start segmenting and profiling the leads in an automated way. Predictive lead scoring aids the processing of leads, by allocating them into groups. However, if you are growing rapidly, predictive lead scoring is definitely a consideration because it will grow with your business.

5. You have low volume and high margin sales situation

Have you heard about the ’80/20′ rule of thumb? That’s where that 80% of your revenue comes from 20% of your lead base. High margin sales typically involve more nurturing. The prize is bigger, but equally is the potential loss (in terms of wasted time and effort) from an unsuccessful deal. As mentioned above, predictive lead scoring can help you nurture contacts through the sales funnel, every step of the way.

Likewise, there’s a proportion of your lead base that will suck-up a disproportionate amount of resources (with no reward). So you want to identify those prospects, and either trim them away or deprioritize them. Although, it’s an often forgotten aspect, predictive lead scoring helps you identify the strongest leads BUT it also helps you weed out the leads that would be a waste of time and resources to chase.

6. You have a complicated product-target relationships

Remember the old adage: What is one man’s trash is another man’s treasure. This can be the case when you have a complicated product-target relationships such as when businesses have multiple products with overlapping user-bases (which is not uncommon). It can then be very hard to figure out who to target with one product, but not another. Predictive lead scoring solves that because it can generate a score for every product. Thus, when a lead receives multiple scores (for example, a customer may score lowly on Product A, very highly on Product B and medium on Product C), the marketing and sales teams can tailor their efforts towards the product that scored the highest and therefore has the best chance of conversion.

7. You want to improve the relationship and integration between marketing and sales

Marketing and sales teams may have different assumptions about what constitutes a good or bad lead (with potential disputes about this). In traditional lead scoring (without the machine algorithms) people had to make assumptions and assign weights/points to different points of data. Predictive lead scoring does away with all that guesswork, and can settle any disputes. Machine learning is entirely objective because it assigns the weights based on data, not intuition.

Also, consider that a better success rate from leads will boost the morale of sales team. Conversely, the actions taken by the sales can feed back into the model (by virtue of the information they collate) so that marketers have richer and more complete information to work with. Implementing predictive lead scoring can be a win-win scenario for both marketing and sales teams, bringing them closer together.

About Matt Steele

Matt has over 14 years of experience in the marketing research arena, with a combination of research experience (qualitative and quantitative), marketing training, academic psychology (cognitive), creative leadership, geekiness and artistic flair. He currently works for Displayr (the home of Q and Displayr) and is based in London: supporting, selling, marketing and training for Q research software and associated software packages (eg: Displayr). He holds a Honours degree in Psychology from UNSW, a Grad Cert. in Marketing from UTS, and a Grad Dip in Directing from NIDA (all based in Sydney, Australia).